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. 2011 Sep;7(9):e1002146.
doi: 10.1371/journal.pcbi.1002146. Epub 2011 Sep 15.

Retracing micro-epidemics of Chagas disease using epicenter regression

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Retracing micro-epidemics of Chagas disease using epicenter regression

Michael Z Levy et al. PLoS Comput Biol. 2011 Sep.

Abstract

Vector-borne transmission of Chagas disease has become an urban problem in the city of Arequipa, Peru, yet the debilitating symptoms that can occur in the chronic stage of the disease are rarely seen in hospitals in the city. The lack of obvious clinical disease in Arequipa has led to speculation that the local strain of the etiologic agent, Trypanosoma cruzi, has low chronic pathogenicity. The long asymptomatic period of Chagas disease leads us to an alternative hypothesis for the absence of clinical cases in Arequipa: transmission in the city may be so recent that most infected individuals have yet to progress to late stage disease. Here we describe a new method, epicenter regression, that allows us to infer the spatial and temporal history of disease transmission from a snapshot of a population's infection status. We show that in a community of Arequipa, transmission of T. cruzi by the insect vector Triatoma infestans occurred as a series of focal micro-epidemics, the oldest of which began only around 20 years ago. These micro-epidemics infected nearly 5% of the community before transmission of the parasite was disrupted through insecticide application in 2004. Most extant human infections in our study community arose over a brief period of time immediately prior to vector control. According to our findings, the symptoms of chronic Chagas disease are expected to be absent, even if the strain is pathogenic in the chronic phase of disease, given the long asymptomatic period of the disease and short history of intense transmission. Traducción al español disponible en Alternative Language Text S1/A Spanish translation of this article is available in Alternative Language Text S1.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Comparison of the fit of multi-epidemic models for describing T. cruzi transmission in Guadalupe, Arequipa, Peru to a single epidemic model.
Shown are the mean and standard error of the estimated Bayes' factors for comparing each model to the 1 epicenter model. The dotted line denotes models with strong support relative to the 1 epicenter model (Bayes' factor>10).
Figure 2
Figure 2. The expected percent of study participants infected over each calendar year back to 1980.
A. The expectation for models with 2,4,6,8 and 10 epicenters; lines are shaded according to the number of epicenters (2 epicenters-light grey, 10 epicenters = black). B. A boxplot showing the median and credible intervals for the posterior estimates from the best-fit four-epicenter regression model. Chagas disease is a lifelong infection; infected individuals are assumed to remain seropositive through their lifetimes.
Figure 3
Figure 3. Estimated geographic position of introductions of Trypanosoma cruzi into the community of Guadalupe, Arequipa, Peru.
The probability that each household was the first (hexagons), second (triangles), third (squares) or fourth (pentagons) site of introduction under the four-epicenter model is shown. Larger shapes correspond to higher probabilities. Households with cases of human disease are shown as red circles, and those with vectors carrying T. cruzi are shown in grey circles. The following steps were taken to protect patient anonymity in making this map: The geographic position of human cases has been slightly and randomly perturbed; the positions of some uninfected households have been altered; and certain regions of the map have been rotated a random angle around their centroid.
Figure 4
Figure 4. The observed and predicted relationship between age and prevalence of Trypanosoma cruzi infection in Guadalupe, Arequipa, Peru.
The histogram represents the observed data; a smoothed spline, weighted by the number of observations at each age, is fit to these data (black curve). Model estimates of the relationship between age and prevalence were calculated by determining the probability of infection for each individual derived from the posterior predictions of the epicenter regression model with four epicenters. The spline fit to the median posterior predictions is surrounded by a region bounded by splines fit to predictions from the 2.5% and 97.5% quantiles of the posterior (light grey, shaded).
Figure 5
Figure 5. The expected number of cases of late-stage Chagas disease among individuals infected with T. cruzi in Guadalupe, Arequipa, Peru in 2004.
Top row: three alternative models to describe the probability of onset of late-stage disease as a function of time (see text). Histograms represent the posterior expected number of late stage cases under the four-epicenter model (middle row) and one-epicenter model (bottom row).

References

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    1. Levy MZ, Kawai V, Bowman NM, Waller LA, Cabrera L, et al. Targeted screening strategies to detect trypanosoma cruzi infection in children. PLoS Negl Trop Dis. 2007;1:e103. - PMC - PubMed
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